This is still a hotly debated topic in the world of analytics – attributing and measuring the various channels that result in sales. Multi-channel attribution is the attempt to attribute influence to different channels that are involved in the sale rather than skewing it in favor of the last source – also called last click attribution. Multi-channel attribution is significant because it helps plan and focus campaigns accordingly – putting more effort and money into channels that have a disproportionate impact on the bottom-line. Accurately identifying the approach taken is crucial for businesses as it helps in identifying the most effective channel and the varying levels of influence.

Though it is virtually impossible to trace multichannel attribution altogether, measuring intent is one of the best ways to weigh the cumulative effect.

Refer to the illustration below …

In this scenario, I have illustrated the path taken by a user to purchasing a game; marked by the blue arrow (I will explain the gray dotted lines later on).

The user is intent on buying a game, but is undecided. He wants to check for game reviews before making up his mind and hence searches for the “game reviews”.

The result takes him to a game review website, and he clicks a website in the top 10 search results. The game review website has an extensive coverage of various games along with ratings (user and editorial) that play an pivotal role in the decision making.

He has short-listed a list of games that are potential candidates for purchase and proceeds to YouTube where he wants to experience how different users experience the game and the various stages (game-play).

He has solidified his final list, but he is keen on buying just one game off the list. He poses a question on various social networks and gathers opinions from peers before making up his mind.

His mind made; he now heads to Google, types keywords that are purchase specific like “buy games” or “buy black ops game” and proceeds to an e-commerce website to make the final purchase.

Reviewing the scenario, it is clear that in the purchase decision, multiple channels were involved, and it is a grave mistake attributing the sale only on the last click.

A plausible approach to multi-channel attribution:

Measuring the intent of purchase along the path taken helps gauge the cumulative impact it can have on the purchase decision. If a purchase decision can be thought of as a glass, the various channels fill it with drops of intent.
In definition, if on an average it takes N number of steps to influence a purchase transaction, the measure of intent m is a value assigned to each of the channels that increments or decrements the number of steps towards N. I shall explain the approach through a couple of examples.

Let’s assume that it takes 7 steps (N=7) to influence a purchase and the measure of intent for each of the channels are as follows: IGN-Game review website (m1=3), YouTube (m2=2), Metacritic (m3=1.5), Facebook (m4=2), Twitter (m5=1), Vimeo (m6=1).

It is evident that the total measure of intent of user A is 7, which is also the average number of steps required (N) to make a purchase and hence user A is likely to make a purchase transaction. On the contrary, because of the path taken by user B, he is half as likely to make a purchase as his measure of intent is only 3.5.

Calculating N and m

This is the tough part, but not so much with the right data. To calculate N (the average number of steps taken to influence a purchase), list all the main sources of traffic and mentions (about your brand/product/service) in a survey form and ask your customers about the sources visited before making a purchase decision. The survey should also question the level of influence the sources had on their purchase decision. Capture this survey immediately after the purchase because the trail is fresh in their mind. This helps in approximating the number of steps and the measure of intent (m) that must be assigned to each of these sources. Measure of intent (m) can also be calculated by checking for purchase elasticity i.e. The quantum of impact a certain source can have on sales. For e.g., A 5% drop in conversations or mentions on Facebook, results in 10% drop in sales.

Though this approach works in most cases, it does not account for multiple to-and-fros (represented in the illustration as dotted grey lines) that users may take before making the decision. On instances when users go back and forth to the same source, there is a marginal diminishing measure of impact (an exponential decay).

This approach requires refinement and needs a large sample size before getting close to a result that is comfortable. In the end, the learning is invaluable, and every channel gets its rightful due.